A Computational Approach to Edge Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Segmentation through Variable-Order Surface Fitting
IEEE Transactions on Pattern Analysis and Machine Intelligence
Motion detection in spatio-temporal space
Computer Vision, Graphics, and Image Processing
Shape recovery and segmentation with deformable part models
Shape recovery and segmentation with deformable part models
An Experimental Comparison of Range Image Segmentation Algorithms
IEEE Transactions on Pattern Analysis and Machine Intelligence
Image segmentation based on oscillatory correlation
Neural Computation
An Adaptive Contour Closure Algorithm and Its Experimental Evaluation
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Saliency sequential surface organization for free-form object recognition
Computer Vision and Image Understanding
| Hi-index | 0.14 |
Due to the potential for essentially unbounded scene complexity, it is often necessary to translate the sensor-derived signals into richer symbolic representations. A key initial stage in this abstraction process is signal-level perceptual organization (SLPO) involving the processes of partitioning and identification. A parallel SLPO algorithm that follows the global hypothesis testing paradigm, but breaks the iterative structure of conventional region growing through the use of alpha -partitioning and region filtering is presented. These two techniques segment an image such that the gray-level variation within each region can be described by a regression model. Experimental results demonstrate the effectiveness of this algorithm.